In [1]}, researchers applied topic modeling on COVID-19–related Reddit comments to extract meaningful categories from public opinions. They then designed an LSTM recurrent neural network [2]} for polarity and sentiment classification. They used \(sentiStrength\)  [3]} for sentiment detection and average out the sentiment of COVID-19 related comments obtained from 10 subreddits forums. They labeled the data as per \(sentiStrength\)  scores and achieved an \(81.15\%\)  classification accuracy. In [4]}, comment data obtained from social media is processed and then context-aware themes are extracted. For each theme, the sentiment is extracted using VADER lexicon-based algorithm [5]} and only themes having positive or negative scores are kept, neutral themes are discarded. Later, these themes are categorized into 34 negative and 20 positive broader themes using human reviewers. These themes span different areas related to health, psychological, and social issues and showed the impact of COVID-19 negatively. The authors also recommend possible solutions for negative themes. In another paper [6]}, the authors also mine Twitter to use collected tweets as a repository to analyze information shared by the people to visualize topics of interest, and the emotions of common citizens in the USA as the pandemic is unfolding.
